XBSeq is a novel algorithm for testing RNA-seq differential expression (DE), where a statistical model was established based on the assumption that observed signals are the convolution of true expression signals and sequencing noises. The mapped reads in non-exonic regions are considered as sequencing noises, which follows a Poisson distribution. Given measurable observed signal and background noise from RNA-seq data, true expression signals, assuming governed by the negative binomial distribution, can be delineated and thus the accurate detection of differential expressed genes.

UCell is a package for evaluating gene signatures in single-cell datasets. UCell signature scores, based on the Mann-Whitney U statistic, are robust to dataset size and heterogeneity, and their calculation demands less computing time and memory than other available methods, enabling the processing of large datasets in a few minutes even on machines with limited computing power. UCell can be applied to any single-cell data matrix, and includes functions to directly interact with SingleCellExperiment and Seurat objects.

This package provides tools for analysis of ChIP-seq and other functional sequencing data.

The Power Law Global Error Model (PLGEM) has been shown to faithfully model the variance-versus-mean dependence that exists in a variety of genome-wide datasets, including microarray and proteomics data. The use of PLGEM has been shown to improve the detection of differentially expressed genes or proteins in these datasets.

This package implements two functions. One of them reads an Affymetrix CDF and creates a hash table environment containing the location/probe set membership mapping. The other one creates a package that automatically loads that environment.

This package provides the output of running Salmon on a set of 24 RNA-seq samples from Alasoo, et al. "Shared genetic effects on chromatin and gene expression indicate a role for enhancer priming in immune response", published in Nature Genetics, January 2018.

Complex heatmaps are efficient to visualize associations between different sources of data sets and reveal potential structures. This package provides a highly flexible way to arrange multiple heatmaps and supports self-defined annotation graphics.

This package contains whole-genome single cell sequencing data for demonstration purposes in the AneuFinder package.

This is an R package to make it easier to import and store phylogenetic trees with associated data; and to link external data from different sources to phylogeny. It also supports exporting phylogenetic trees with heterogeneous associated data to a single tree file and can be served as a platform for merging tree with associated data and converting file formats.

This package provides high level functions for reading Affy .CEL files, phenotypic data, and then computing simple things with it, such as t-tests, fold changes and the like. It makes heavy use of the affy library. It also has some basic scatter plot functions and mechanisms for generating high resolution journal figures.

This package provides a data package containing summarized Illumina 450k array data on 2800 samples and summarized EPIC data for 2620 samples. The data can be use as a background data set in the interactive application.

This is a package for saving SummarizedExperiments into file artifacts, and loading them back into memory. This is a more portable alternative to serialization of such objects into RDS files. Each artifact is associated with metadata for further interpretation; downstream applications can enrich this metadata with context-specific properties.

This package provides processed and raw count data for single-cell RNA sequencing. In addition, this package offers single-cell ATAC-seq, and seqFISH (spatial transcriptomic) experiments performed along a timecourse of mouse gastrulation and early organogenesis.

BgeeCall allows generating present/absent gene expression calls without using an arbitrary cutoff like TPM<1. Calls are generated based on reference intergenic sequences. These sequences are generated based on expression of all RNA-Seq libraries of each species integrated in Bgee.

The STRINGdb package provides an R interface to the STRING protein-protein interactions database. STRING is a database of known and predicted protein-protein interactions. The interactions include direct (physical) and indirect (functional) associations. Each interaction is associated with a combined confidence score that integrates the various evidences.

The aim of TCGAbiolinks is:

1. facilitate GDC open-access data retrieval;

2. prepare the data using the appropriate pre-processing strategies;

3. provide the means to carry out different standard analyses, and;

4. to easily reproduce earlier research results.

In more detail, the package provides multiple methods for analysis (e.g., differential expression analysis, identifying differentially methylated regions) and methods for visualization (e.g., survival plots, volcano plots, starburst plots) in order to easily develop complete analysis pipelines.

TrackViewer offers multi-omics analysis with web based tracks and lollipops. Visualize mapped reads along with annotation as track layers for NGS datasets such as ChIP-seq, RNA-seq, miRNA-seq, DNA-seq, SNPs and methylation data.

This package defines an S4 class for storing data from single-cell experiments. This includes specialized methods to store and retrieve spike-in information, dimensionality reduction coordinates and size factors for each cell, along with the usual metadata for genes and libraries.

This package speeds up the derfinder package when using multiple cores. It is particularly useful when using BiocParallel and it helps reduce the time spent loading the full derfinder package when running the F-statistics calculation in parallel.

This package includes positive ionization mode data in NetCDF file format. Centroided subset from 200-600 m/z and 2500-4500 seconds. Data originally reported in "Assignment of Endogenous Substrates to Enzymes by Global Metabolite Profiling" Biochemistry; 2004; 43(45). It also includes detected peaks in an xcmsSet.

This package is an implementation of the Adaptively Weighted Fisher's method, including fast p-value computing, variability index, and meta-pattern.

The QFeatures infrastructure enables the management and processing of quantitative features for high-throughput mass spectrometry assays. It provides a familiar Bioconductor user experience to manages quantitative data across different assay levels (such as peptide spectrum matches, peptides and proteins) in a coherent and tractable format.

This package defines classes for "class discovery" in the OOMPA project. Class discovery primarily consists of unsupervised clustering methods with attempts to assess their statistical significance.

This package provides a collection of reference expression datasets with curated cell type labels, for use in procedures like automated annotation of single-cell data or deconvolution of bulk RNA-seq.